LOW COST IoT SYSTEM FOR THE ASSET CONTROL SUPPORT BASED ON BARCODE SCANNING
Bibliographic record
Abstract
Purpose. The goal of the paper is to describe analysis and implementation IoT system for support the asset control via barcode scanning. Originality. The paper deals with the research on surveys for development an IoT device for searching correct store location of the devices in the laboratory and support asset checking for selected location. Methodology. The paper proposes one of the possibilities for development an IoT device basing on ESP8266 using Nextion intelligent display and a Windows application developed using C#. Retrieving data from a remote database, the application updates the data from central server. Authors described the whole development process starting from computer design of the proposed IoT device, chose the elements for hardware unit, design and implementation the Windows application and also experimental verification of derived results. Result. In this work authors proposed experimental sample of IoT system for the asset control via barcode scanning. The client-server application was designed to support the control of property records with the design of IoT equipment. The design of IoT devices is realized by modular connection of components. By implementing the GUI on the display, it is possible to control the reader module and observe the records in the informative mode and the control mode. 3D models are a device in a housing, where the output is a display. Using developed application, it is possible to connect to an IoT device and perform asset registration control by communicating with each other. This system implements all theoretical results described in the paper, and confirms them basing on the experiments provided. Practical value. Proposed IoT system could be practically used in university laboratories to control equipment location at any moment of time. References 11, figures 14.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".